Human exposure modelling for chemical risk assessment: a review of current approaches and research and policy implications

暴露评估 一致性(知识库) 过程(计算) 多样性(控制论) 优势和劣势 风险评估 骨料(复合) 产品(数学) 政府(语言学) 计算模型 风险分析(工程) 管理科学 计算机科学 业务 环境卫生 心理学 工程类 几何学 材料科学 复合材料 人工智能 哲学 数学 语言学 操作系统 计算机安全 社会心理学 医学 模拟
作者
Michael E. Fryer,Chris D. Collins,Helen Ferrier,R.N. Colvile,Mark Nieuwenhuijsen
出处
期刊:Environmental Science & Policy [Elsevier BV]
卷期号:9 (3): 261-274 被引量:228
标识
DOI:10.1016/j.envsci.2005.11.011
摘要

Abstract A wide variety of exposure models are currently employed for health risk assessments. Individual models have been developed to meet the chemical exposure assessment needs of Government, industry and academia. These existing exposure models can be broadly categorised according to the following types of exposure source: environmental, dietary, consumer product, occupational, and aggregate and cumulative. Aggregate exposure models consider multiple exposure pathways, while cumulative models consider multiple chemicals. In this paper each of these basic types of exposure model are briefly described, along with any inherent strengths or weaknesses, with the UK as a case study. Examples are given of specific exposure models that are currently used, or that have the potential for future use, and key differences in modelling approaches adopted are discussed. The use of exposure models is currently fragmentary in nature. Specific organisations with exposure assessment responsibilities tend to use a limited range of models. The modelling techniques adopted in current exposure models have evolved along distinct lines for the various types of source. In fact different organisations may be using different models for very similar exposure assessment situations. This lack of consistency between exposure modelling practices can make understanding the exposure assessment process more complex, can lead to inconsistency between organisations in how critical modelling issues are addressed (e.g. variability and uncertainty), and has the potential to communicate mixed messages to the general public. Further work should be conducted to integrate the various approaches and models, where possible and regulatory remits allow, to get a coherent and consistent exposure modelling process. We recommend the development of an overall framework for exposure and risk assessment with common approaches and methodology, a screening tool for exposure assessment, collection of better input data, probabilistic modelling, validation of model input and output and a closer working relationship between scientists and policy makers and staff from different Government departments. A much increased effort is required is required in the UK to address these issues. The result will be a more robust, transparent, valid and more comparable exposure and risk assessment process.

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